{"id":"W4245720599","doi":"10.36227/techrxiv.16973650","title":"Random Fourier Feature Based Deep Learning for Wireless Communications","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Speech and Audio Processing","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Robustness (evolution); Kernel (algebra); Fourier transform; Wireless; Computer science; Artificial intelligence; Convergence (economics); Feature (linguistics); Kernel method; Algorithm; Pattern recognition (psychology); Mathematics; Telecommunications; Support vector machine; Discrete mathematics; Mathematical analysis","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004288986,0.0002301711,0.0003569324,0.00009656051,0.0004420154,0.001035447,0.002424555,0.0002723976,0.00002271969],"category_scores_gemma":[0.000179907,0.0002138176,0.0002498314,0.0002246059,0.00005087257,0.0002040363,0.001878326,0.0008925911,0.000004829934],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004916166,"about_ca_system_score_gemma":0.0003973951,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001202582,"about_ca_topic_score_gemma":0.00006288762,"domain_scores_codex":[0.9985479,0.0001431276,0.0002178942,0.0005622507,0.0002333397,0.0002954543],"domain_scores_gemma":[0.9970884,0.0004677717,0.0002048159,0.001796312,0.0003456796,0.00009700586],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00004876772,0.0002180964,0.0007090826,0.0007111934,0.0001915915,0.00001900336,0.001563947,0.01407949,0.002439484,0.002153766,0.003402821,0.9744627],"study_design_scores_gemma":[0.001362802,0.00001775134,0.00005569459,0.0002846902,0.00003239494,0.000005559322,0.0001014794,0.9596394,0.0195253,0.001346051,0.01720711,0.0004218124],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0005910827,0.001871822,0.9861217,0.007362781,0.0003502207,0.0003504092,0.000001921627,0.0003266026,0.003023492],"genre_scores_gemma":[0.1067485,0.00008867086,0.8902055,0.0009771322,0.00009912464,0.0001874754,0.0001610199,0.00002218212,0.00151041],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9740409,"threshold_uncertainty_score":0.9984841,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02524193663976559,"score_gpt":0.2820052568562753,"score_spread":0.2567633202165097,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}